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Signal Temporal Logic-Guided Apprenticeship Learning
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations.
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots.
Learning from Demonstrations using Signal Temporal Logic
Learning-from-demonstrations is an emerging paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions.
Mining Environment Assumptions for Cyber-Physical System Models
We assume that the correctness of each component can be specified as a requirement satisfied by the output signals produced by the component, and that such an output guarantee is expressed in a real-time temporal logic such as Signal Temporal Logic (STL).
Interpretable Classification of Time-Series Data using Efficient Enumerative Techniques
Cyber-physical system applications such as autonomous vehicles, wearable devices, and avionic systems generate a large volume of time-series data.
